Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

Abstract

As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre‐summer rainy season, an experimental convection‐permitting ensemble prediction system (GM‐CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM‐CPEPS produces 12 h forecasts at 0.03° horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time‐lagged scheme and topography are combined to generate the initial perturbations. Sea‐surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi‐physics, and perturbed parameters are all implemented. In this study, GM‐CPEPS was verified over a 15‐day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM‐CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability‐matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal‐member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal‐probability and neighbourhood‐probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.

Practical and Intrinsic Predictability of a Warm‐Sector Torrential Rainfall Event in the South China Monsoon Region

“…However, it should be noted that the practical predictability of severe weather is also influenced by some other factors in NWP models, such as horizontal resolution, physical parameterization schemes, and topographic effects (Surcel et al, ; Zhang et al, ; Burlingame et al, ). Recently, based on a 15‐day period during the SCMREX in May 2014, Zhang () specifically designed a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China and found that initial perturbations, LBCs, and model physics were all very sensitive to different heavy rainfall events. Therefore, to improve the operational forecasting ability of warm‐sector torrential rainfall, it is necessary to carry out more detailed studies with more operational practices and more flow‐dependent cases (Melhauser & Zhang, ; Nielsen & Schumacher, ).…”

The effects of cloud–aerosol interaction complexity on simulations of presummer rainfall over southern China

“…The region receives the majority of its annual rainfall at this time of year, mainly from warm-sector convection. The synoptic situation studied here is one of the most frequently occurring modes of convective organisation in the region (Huang, 2018); hence understanding whether cloud-aerosol interactions can affect the rainfall produced by such systems may have implications for improving predictions of regional rainfall extremes (Luo et al, 2017;Zhang et al, 2018). Moreover, since future generations of operational weather forecast models will be able to include two-way coupling of clouds are aerosols, it is our intention to contribute evidence regarding the role of two-way coupling in short-range predictions of precipitation extremes.…”

Assimilation of Data Derived from Optimal-Member Products of TREPS for Convection-Permitting TC Forecasting over Southern China

“…FSS was calculated for varying precipitation thresholds over different neighborhood lengths. However, only the 50-km-length FSS was compared between various forecasts here, as in [56], because the conclusions based on the comparison of FSS with other neighborhood lengths are nearly the same. FSS ranges from 0 to 1, with higher values corresponding to higher skills.…”

“…Similar to [56], rainfall with precipitation rates greater than 0.1, 10, 20, and 40 mm h −1 is defined as light, moderate, heavy, and extremely heavy rainfall, respectively, in this study.…”